import re
import numpy as np
import pandas as pd
from pprint import pprint

# Gensim
import gensim
import gensim.corpora as corpora
from gensim.utils import simple_preprocess
from gensim.models import CoherenceModel

# Plotting tools
import pyLDAvis
import pyLDAvis.gensim  # don't skip this
import matplotlib.pyplot as plt

# spacy for lemmatization
import spacy
import json
import warnings
import networkx as nx

warnings.filterwarnings("ignore",category=DeprecationWarning)

import nltk
nltk.download('stopwords')
nltk.download('words')

# NLTK Stop words
from nltk.corpus import stopwords
stop_words = stopwords.words('english')

from nltk.corpus import words
eng_words = words.words('en')

from langdetect import detect
# to enforce consistent results, check github langdetect readme
from langdetect import DetectorFactory
DetectorFactory.seed = 0


import os 

import csv

[nltk_data] Downloading package stopwords to
[nltk_data]     /home/siyanththan_v/nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package words to
[nltk_data]     /home/siyanththan_v/nltk_data...
[nltk_data]   Package words is already up-to-date!

df = pd.read_csv('mount/TweetsOnUser.csv', lineterminator='\n', low_memory=False)
df.head()

tweets = df.TweetText.values.tolist()
userIds = df.userid.values.tolist()
print("PLS")

PLS

dict = {}

for i in range(len(userIds)):
    if userIds[i] not in dict:
        dict[userIds[i]] = []
    for word in str(tweets[i]).split(" "):
        dict[userIds[i]].append(word)

data_final = list(dict.values())

# Create Dictionary
id2word = corpora.Dictionary(data_final)

# Create Corpus
# type(data_lemmatized)
# print (data_lemmatized)
texts = data_final

print(data_final[:1])

[['you!', 'I', 'love', 'xoxo', 'you!', 'I', 'proud', 'of', 'you', 'I', 'love', 'you', 'my', 'day', 'day.', 'After', 'After', 'all', 'all,', 'I', 'was', 'I', 'was', 'born', 'spread', 'I', 'am', 'love', "I'm", 'sadness', 'today', 'who', 'I', 'am', "I'm", 'thankful', 'everything', 'everyone', 'good', 'and', 'the', 'bad', 'happiness']]

# Term Document Frequency
corpus = [id2word.doc2bow(text) for text in texts]

# Human readable format of corpus (term-frequency)
print([[(id2word[id], freq) for id, freq in cp] for cp in corpus[:1]])

# Build LDA model
lda_model = gensim.models.ldamulticore.LdaMulticore(corpus=corpus,id2word=id2word,num_topics=30, passes=10, workers=3)

# Print the Keyword in the 10 topics
pprint(lda_model.print_topics())
doc_lda = lda_model[corpus]

# Compute Perplexity
print('\nPerplexity: ', lda_model.log_perplexity(corpus))  # a measure of how good the model is. lower the better.

[[('After', 2), ('I', 7), ("I'm", 2), ('all', 1), ('all,', 1), ('am', 2), ('and', 1), ('bad', 1), ('born', 1), ('day', 1), ('day.', 1), ('everyone', 1), ('everything', 1), ('good', 1), ('happiness', 1), ('love', 3), ('my', 1), ('of', 1), ('proud', 1), ('sadness', 1), ('spread', 1), ('thankful', 1), ('the', 1), ('today', 1), ('was', 2), ('who', 1), ('xoxo', 1), ('you', 2), ('you!', 2)]]
[(3,
  '0.019*"México" + 0.013*"Mexico" + 0.012*"Calderón" + 0.011*"Diego" + '
  '0.010*"Mty" + 0.009*"ALOHA" + 0.008*"Escobedo" + 0.008*"Marisela" + '
  '0.007*"PAN" + 0.007*"SURF"'),
 (4,
  '0.007*"Google" + 0.006*"Facebook" + 0.005*"You" + 0.005*"Twitter" + '
  '0.005*"Social" + 0.005*"iPhone" + 0.004*"iPad" + 0.004*"Apple" + '
  '0.004*"How" + 0.004*"Media"'),
 (28,
  '0.032*"cont" + 0.027*"RT" + 0.021*"di" + 0.014*"Indonesia" + 0.012*"yg" + '
  '0.012*"Jakarta" + 0.012*"dan" + 0.009*"yang" + 0.009*"ke" + 0.009*"ini"'),
 (9,
  '0.015*"free" + 0.012*"We" + 0.012*"today" + 0.011*"here" + 0.010*"Dec" + '
  '0.010*"us" + 0.010*"Christmas" + 0.009*"now" + 0.009*"win" + 0.008*"to"'),
 (24,
  '0.024*"na" + 0.015*"sa" + 0.011*"di" + 0.010*"ko" + 0.010*"ang" + '
  '0.009*"il" + 0.007*"ng" + 0.007*"ako" + 0.006*"lang" + 0.006*"ka"'),
 (23,
  '0.352*"RT" + 0.069*"" + 0.020*"http" + 0.008*"via" + 0.006*"you" + '
  '0.005*":" + 0.004*"out" + 0.003*"Christmas" + 0.003*"htt" + 0.003*"ht"'),
 (29,
  '0.016*"Vegas" + 0.011*"Las" + 0.009*"JYJ" + 0.007*"NEWS" + 0.007*"Concert" '
  '+ 0.006*"Japan" + 0.006*"TVXQ" + 0.005*"Japanese" + 0.005*"Music" + '
  '0.005*"Awards"'),
 (19,
  '0.019*"London" + 0.018*"Wall" + 0.018*"Street" + 0.017*"Paris" + '
  '0.016*"der" + 0.015*"die" + 0.014*"Manhattan" + 0.013*"NYC" + '
  '0.013*"Berlin" + 0.012*"Art"'),
 (26,
  '0.547*"" + 0.028*"Follow" + 0.026*"followers" + 0.017*"Team" + 0.017*"Back" '
  '+ 0.016*"am" + 0.015*"getting" + 0.011*"FOLLOW" + 0.011*"i" + 0.010*"day"'),
 (8,
  '0.116*"Indy" + 0.115*"Como" + 0.115*"Mundo" + 0.112*"Laila" + 0.027*"FF" + '
  '0.010*"vídeo" + 0.009*"Pega" + 0.009*"uma" + 0.008*"nesse" + 0.008*"Paz"'),
 (12,
  '0.225*"week" + 0.175*"Ranking" + 0.175*"FollowFriday" + 0.042*"this" + '
  '0.041*"Celebrity" + 0.041*"Brazilian" + 0.040*"Twitter" + 0.017*"French" + '
  '0.012*"Irish" + 0.011*"Chilean"'),
 (25,
  '0.082*"Haiti" + 0.017*"rt" + 0.015*"haiti" + 0.015*"Aid" + 0.013*"Bush" + '
  '0.012*"VAW" + 0.012*"Pls" + 0.011*"Violence" + 0.010*"vaw" + 0.009*"LP"'),
 (21,
  '0.024*"Obama" + 0.024*"p2" + 0.020*"GOP" + 0.015*"tcot" + 0.013*"tax" + '
  '0.011*"DADT" + 0.011*"Senate" + 0.010*"Palin" + 0.008*"President" + '
  '0.008*"House"'),
 (22,
  '0.007*"RT" + 0.007*"Korea" + 0.007*"US" + 0.006*"WikiLeaks" + 0.006*"China" '
  '+ 0.005*"BBC" + 0.004*"will" + 0.004*"South" + 0.004*"of" + 0.004*"news"'),
 (17,
  '0.009*"Christmas" + 0.008*"New" + 0.007*"Video" + 0.006*"the" + 0.005*"TV" '
  '+ 0.004*"Music" + 0.004*"The" + 0.004*"of" + 0.004*"Show" + 0.004*"Happy"'),
 (15,
  '0.029*"van" + 0.028*"op" + 0.020*"een" + 0.016*"met" + 0.015*"het" + '
  '0.010*"niet" + 0.008*"en" + 0.008*"Daughter" + 0.008*"naar" + 0.008*"dat"'),
 (10,
  '0.034*"you" + 0.009*"the" + 0.009*"I" + 0.009*"can" + 0.009*"will" + '
  '0.007*"we" + 0.007*"Christmas" + 0.006*"one" + 0.006*"do" + 0.006*"who"'),
 (5,
  '0.033*"la" + 0.029*"en" + 0.028*"que" + 0.025*"el" + 0.015*"del" + '
  '0.015*"RT" + 0.015*"un" + 0.014*"para" + 0.013*"por" + 0.013*"con"'),
 (27,
  '0.240*"News" + 0.194*"fb" + 0.073*"Breaking" + 0.053*"quote" + 0.021*"dd" + '
  '0.021*"Financial" + 0.008*"top" + 0.008*"quotes" + 0.007*"Dyer" + '
  '0.006*"Yahoo!"'),
 (11,
  '0.020*"game" + 0.009*"NFL" + 0.008*"win" + 0.007*"team" + 0.007*"vs" + '
  '0.007*"football" + 0.007*"will" + 0.006*"play" + 0.006*"season" + '
  '0.005*"out"')]

Perplexity:  -9.828505188169244

# def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data_lemmatized):
#     # Init output
#     sent_topics_df = pd.DataFrame()

#     # Get main topic in each document
#     for i, row in enumerate(ldamodel[corpus]):
#         print(row) 
#         topics = []
#         row = sorted(row, key=lambda x: (x[1]), reverse=True) 
#         for j, (topic_num, prop_topic) in enumerate(row):
#             if j in [0,1,2]: 
#                 topics.append(topic_num)
#         # Get the Dominant topic, Perc Contribution and Keywords for each document
#         for j, (topic_num, prop_topic) in enumerate(row):
# #             if j in [0,1,2]: #top 3 dominant topics
#             if j == 0:  # => dominant topic
#                 wp = ldamodel.show_topic(topic_num)
#                 topic_keywords = ", ".join([word for word, prop in wp])
#                 sent_topics_df = sent_topics_df.append(pd.Series([topics, round(prop_topic,4), topic_keywords]), ignore_index=True)
#             else:
#                 break
#     sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']

def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data_final):
    # Init output
    sent_topics_df = pd.DataFrame()

    # Get main topic in each document
    for i, row in enumerate(ldamodel[corpus]):
        row = sorted(row, key=lambda x: (x[1]), reverse=True)
        # Get the Dominant topic, Perc Contribution and Keywords for each document
        for j, (topic_num, prop_topic) in enumerate(row):
            if j == 0:  # => dominant topic
                wp = ldamodel.show_topic(topic_num)
                topic_keywords = ", ".join([word for word, prop in wp])
                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)
            else:
                break
    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']

    # Add original text to the end of the output
    contents = pd.Series(texts)
    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)
    return(sent_topics_df)


df_topic_sents_keywords = format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data_final)

# Format
df_dominant_topic = df_topic_sents_keywords.reset_index()
df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']
#TER
# Show
df_dominant_topic.head(10)

userId_final = list(dict.keys())
userIdsCol = pd.DataFrame(userId_final)
userIdsCol.columns = ['User_Id']
userIdsCol.head(10)

df_final = df_dominant_topic.merge(userIdsCol, left_index=True, right_index=True)
df_final.head(10)

G=nx.Graph()
for row in df_final.itertuples(index=False):
    G.add_node(row.User_Id)
    if row.Dominant_Topic not in G:
        G.add_node(row.Dominant_Topic)
    G.add_edge(row.User_Id, row.Dominant_Topic, weight=row.Topic_Perc_Contrib)

# Takes a while for graph to be displayed, cpu/ram increases so 
# instead of running this, printing out the nodes and edges is easier to
# inspect the graph

# nx.draw(G)
# plt.show()

for line in nx.generate_edgelist(G):
    print (line)

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132723

print(G.number_of_edges()) 
# gives 132723 edges, same as the number of user_ids

132723

	id	TweetText	userid	creationtimestamp
0	7515433917751296	Enjoy TSA Pat Down RT Hey everybody Thanksgivi...	194306325	2010-11-24 19:26:33 UTC
1	18960074751868928	Via RT Breaking News Assange signs book deal W...	86711072	2010-12-26 09:23:28 UTC
2	17700051287547904	state of heart soul your soul Birthing child m...	39126647	2010-12-22 21:56:35 UTC
3	21446749583314945	Sweet dreams	23262180	2011-01-02 06:04:38 UTC
4	18528789873041408	dramatic real Operation Clean-Up Now RT CRAP R...	71496690	2010-12-25 04:49:42 UTC

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0	0	10.0	0.6732	you, the, I, can, will, we, Christmas, one, do...	[you!, I, love, xoxo, you!, I, proud, of, you,...
1	1	13.0	0.6472	I, you, RT, me, I'm, lol, my, up, can, it	[H., Jackson, Brown,, Jr, screwdriver, RT, Inf...
2	2	21.0	0.5281	Obama, p2, GOP, tcot, tax, DADT, Senate, Palin...	[opinion, Sen, Cornyn, Repeal, Don’t, Ask,Don’...
3	3	22.0	0.5404	RT, Korea, US, WikiLeaks, China, BBC, will, So...	[Hope, stepson, hear, people, power, power, is...
4	4	17.0	0.3131	Christmas, New, Video, the, TV, Music, The, of...	[Follow, Official, Goon, Gang, Djs, Week, 1, T...
5	5	10.0	0.6154	you, the, I, can, will, we, Christmas, one, do...	[charity:water, Paul, 1:1, 1, time, with, me, ...
6	6	10.0	0.8063	you, the, I, can, will, we, Christmas, one, do...	[Blue, Laws, Christmas, outlawed, Boston, you,...
7	7	17.0	0.4683	Christmas, New, Video, the, TV, Music, The, of...	[Pink, Floyd, Wall, concert, last, night, pret...
8	8	10.0	0.5733	you, the, I, can, will, we, Christmas, one, do...	[Search, the, best, doctor, near, you, fb, wei...
9	9	5.0	0.7123	la, en, que, el, del, RT, un, para, por, con	[Prototype, lanzó, su, 2011, e, Ivan, de, Pine...

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